By way of background concerning some conventional systems, computing devices have traditionally stored information and associated applications and data services locally to the device. Yet, with the evolution of on-line and cloud services, information is increasingly being moved to network providers who perform none, some or all of service on behalf of devices. The evolution of network storage farms capable of storing terabytes of data (with potential for petabytes, exabytes, etc. of data in the future) has created an opportunity to mimic the local scenario in a cloud, with separation of the primary device and the external storage.
However, no cloud service or network storage provider has been able to effectively provide information as a service on any platform, with publishers, developers, and consumers easily publishing, specializing applications for and consuming any kind of data, in a way that can be tracked and audited for all involved. In addition, due to the disparate number of content providers and their typically proprietary schemas for defining data, today, where disparate content providers do not coordinate their publishing acts to the cloud with one another, there is little opportunity to leverage the collective power of such disparate publishing acts. In effect, to the consuming audience of the publishing, such as subscribers or developers, two different data sets look like they have two different shapes, even where there may be some or substantial overlap of data or data types.
More specifically, currently, the way data is uploaded by different parties is such that a similar column of different data sets (e.g., both representing city), if even named at all, will have different names, which may or may not be similar. In either case, human intervention into the underlying data and verification is the conventional way to determine what columns should be named the same or similarly, or to otherwise determine what the relationship between the types or columns of data might be. In addition to being unscalable, such intervention in the underlying data may not be desirable to publishers who want to guarantee their data remains unaltered, as may be the case for a host of proprietary data sets that might be published to the cloud.
The above-described deficiencies of today's devices and data services are merely intended to provide an overview of some of the problems of conventional systems, and are not intended to be exhaustive. Other problems with the state of the art and corresponding benefits of some of the various non-limiting embodiments may become further apparent upon review of the following detailed description.